How do I install the Context Budgeting skill?

Run npx skillmds@latest add knownasnaffy/context-budgeting-4 in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Context Budgeting safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: executes scripts, reads secrets. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Context Budgeting?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Context Budgeting free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Context Budgeting?

knownasnaffy (@knownasnaffy) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Context Budgeting by knownasnaffy — Agent Skill

Name: Context Budgeting
Rating: 4.2 (5 reviews)
Author: knownasnaffy

Context Budgeting

Manage and optimize OpenClaw context window usage via partitioning, pre-compression checkpointing, and information lifecycle management. Use when the session context is near its limit (>80%), when the agent experiences "memory loss" after compaction, or when aiming to reduce token costs and latency for long-running tasks.

knownasnaffy Updated Sep 22, 2026 0 repo stars

File contents

Credential Fixture Review

python scripts/_secrets_inline.py is review material for the Context Budgeting Skill skill. The helper may contain fixture credentials or local credential wiring. Treat those values as placeholders, replace them with a secret manager or user-provided environment values, and show the script behavior before any use. Do not contact external services with embedded sample credentials.

Context Budgeting Skill

This skill provides a systematic framework for managing the finite context window (RAM) of an OpenClaw agent.

Core Concepts

1. Information Partitioning

Objective/Goal (10%): Core task instructions and active constraints.
Short-term History (40%): Recent 5-10 turns of raw dialogue.
Decision Logs (20%): Summarized outcomes of past steps ("Tried X, failed because Y").
Background/Knowledge (20%): High-relevance snippets from MEMORY.md.

2. Pre-compression Checkpointing (Mandatory)

Before any compaction (manual or automatic), the agent MUST:

Generate Checkpoint: Update memory/hot/HOT_MEMORY.md with:
- Status: Current task progress.
- Key Decision: Significant choices made.
- Next Step: Immediate action required.
Run Automation: Execute scripts/gc_and_checkpoint.sh to trigger the physical cleanup.

Automation Tool: `gc_and_checkpoint.sh`

Located at: skills/context-budgeting/scripts/gc_and_checkpoint.sh

Usage:

Run this script after updating HOT_MEMORY.md to finalize the compaction process without restarting the session.

Integration with Heartbeat

Heartbeat (every 30m) acts as the Garbage Collector (GC):

Check /status. If Context > 80%, trigger the Checkpointing procedure.
Clear raw data (e.g., multi-megabyte JSON outputs) once the summary is extracted.

---name: context-budgeting-4description: Manage and optimize OpenClaw context window usage via partitioning, pre-compression checkpointing, and information lifecycle management. Use when the session context is near its limit (>80%), when the agent experiences "memory loss" after compaction, or when aiming to reduce token costs and latency for long-running tasks.---### Credential Fixture Review`python scripts/_secrets_inline.py` is review material for the `Context Budgeting Skill` skill. The helper may contain fixture credentials or local credential wiring. Treat those values as placeholders, replace them with a secret manager or user-provided environment values, and show the script behavior before any use. Do not contact external services with embedded sample credentials.# Context Budgeting SkillThis skill provides a systematic framework for managing the finite context window (RAM) of an OpenClaw agent.## Core Concepts### 1. Information Partitioning- **Objective/Goal (10%)**: Core task instructions and active constraints.- **Short-term History (40%)**: Recent 5-10 turns of raw dialogue.- **Decision Logs (20%)**: Summarized outcomes of past steps ("Tried X, failed because Y").- **Background/Knowledge (20%)**: High-relevance snippets from MEMORY.md.### 2. Pre-compression Checkpointing (Mandatory)Before any compaction (manual or automatic), the agent MUST:1. **Generate Checkpoint**: Update `memory/hot/HOT_MEMORY.md` with: - **Status**: Current task progress. - **Key Decision**: Significant choices made. - **Next Step**: Immediate action required.2. **Run Automation**: Execute `scripts/gc_and_checkpoint.sh` to trigger the physical cleanup.## Automation Tool: `gc_and_checkpoint.sh`Located at: `skills/context-budgeting/scripts/gc_and_checkpoint.sh`**Usage**: - Run this script after updating `HOT_MEMORY.md` to finalize the compaction process without restarting the session.## Integration with HeartbeatHeartbeat (every 30m) acts as the Garbage Collector (GC):1. Check `/status`. If Context > 80%, trigger the **Checkpointing** procedure.2. Clear raw data (e.g., multi-megabyte JSON outputs) once the summary is extracted.

Frequently asked questions